Authors:
Heiko Hoffmann
;
Georgios Petkos
;
Sebastian Bitzer
and
Sethu Vijayakumar
Affiliation:
Institute of Perception, Action and Behavior, School of Informatics, University of Edinburgh, United Kingdom
Keyword(s):
Adaptive control, context switching, Kalman filter, force sensor, robot simulation.
Related
Ontology
Subjects/Areas/Topics:
Adaptive Signal Processing and Control
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Machine Learning in Control Applications
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
Adaptive motor control under continuously varying context, like the inertia parameters of a manipulated object, is an active research area that lacks a satisfactory solution. Here, we present and compare three novel strategies for learning control under varying context and show how adding tactile sensors may ease this task. The first strategy uses only dynamics information to infer the unknown inertia parameters. It is based on a probabilistic generative model of the control torques, which are linear in the inertia parameters. We demonstrate this inference in the special case of a single continuous context variable – the mass of the manipulated object. In the second strategy, instead of torques, we use tactile forces to infer the mass in a similar way. Finally, the third strategy omits this inference – which may be infeasible if the latent space is multi-dimensional – and directly maps the state, state transitions, and tactile forces onto the control torques. The additional tactile i
nput implicitly contains all control-torque relevant properties of the manipulated object. In simulation, we demonstrate that this direct mapping can provide accurate control torques under multiple varying context variables.
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